Research On Multi-Objective Slotting Optimization Based On Improved NSGA-Ⅱ Algorithm

Automated storage and retrieval system is the main form of modernized warehousing,with high space utilization,low storage costs,fast order response,high operational accuracy.Automated storage and retrieval system is a complex system,the storage of a large number of types of goods,if the goods are not a reasonable allocation of cargo space,will inevitably lead to storage operations congestion,chaos,which leads to the entire warehouse operation efficiency is low.Automated storage and retrieval system applications are very wide,China’s demand for Automated storage and retrieval systems is huge,so the Automated storage and retrieval system to carry out research is of great practical significance and industrial application value.The scientific distribution of goods for the optimization of cargo space research is the core of the automated storage and retrieval system research in many sub-fields,in the automated storage and retrieval system management activities occupy an important position.Based on the previous research on the slotting allocation of automated storage and retrieval system,this thesis proposes a more universal multi-objective optimization model,and proposes a solution algorithm with an improvement strategy,the main research content and conclusions of this thesis are as follows:(1)A generalized multi-objective slotting optimization model with workload balance objective is proposedAccording to the storage characteristics of a unitary warehouse,a multi-objective slotting optimization model with workload balancing objectives is proposed,which based on the slotting allocation principle.Different from the common models that only consider the access efficiency and shelf stability,this thesis also considers the workload balance of the aisle access equipment and proposes a multi-objective optimization model that simultaneously considers the access efficiency and shelf stability as well as the workload balance of the stacker cranes.In order to have a better generality,the model is uniquely designed so that it can not be invalidated by the different or mixed use of the actual access equipment in the automated storage and retrieval system,and has the same application and reference value for automated storage and retrieval systems using other operation equipment.(2)Designing improvement strategies for the standard NSGA-Ⅱ algorithmBased on the study of the standard NSGA-II algorithm,we analyze the limitations of the algorithm and design two improvement strategies for the optimization of the standard NSGAII algorithm,including the addition of an adaptive scaling factor and the addition of a neighborhood search strategy.The adaptive scaling factor acts on the crossover operator to control the adaptive change ability of the number of selected gene loci in the early stage and the late stage,which balances the global search and local search;and the introduction of the neighborhood search strategy,which designs three kinds of neighborhood structures to carry out the variable neighborhood operation for each generation of the new population,can guide the solution to jump out of the local optimum,and improve the quality of the solution and the convergence speed of the algorithm.The performance of the algorithm is tested using the Hypervolume metric evaluation method and seven kinds of multi-objective test functions to prove the effectiveness of the improved strategy,and the results show that the improved NSGA-II algorithm performs better.